import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

df1=pd.read_csv("NASA_Exoplanet_Composite.csv") #5483
df2= pd.read_csv("NASA_Exoplanet_Planetary.csv")

C:\Users\User\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3165: DtypeWarning: Columns (4,5) have mixed types.Specify dtype option on import or set low_memory=False.
  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,

df1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5483 entries, 0 to 5482
Columns: 313 entries, rowid to pl_ntranspec
dtypes: float64(212), int64(23), object(78)
memory usage: 13.1+ MB

df2.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 34951 entries, 0 to 34950
Columns: 287 entries, rowid to pl_ntranspec
dtypes: float64(235), int64(25), object(27)
memory usage: 76.5+ MB

columns_df1 = set(df1.columns)
columns_df2 = set(df2.columns)

# Find matching columns
matching_columns = columns_df1.intersection(columns_df2)

print("Matching Columns:")

print(len(matching_columns))

Matching Columns:
254

planets=pd.merge(df1, df2,how="outer")
planets.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 40434 entries, 0 to 40433
Columns: 346 entries, rowid to releasedate
dtypes: float64(237), int64(23), object(86)
memory usage: 107.0+ MB

#finding empty columns
empty_columns = [col for col in planets.columns if planets[col].isna().all()]


print("Completely Empty Columns:")
print(empty_columns)

Completely Empty Columns:
['sy_kepmagerr1', 'sy_kepmagerr2']

p2=planets.drop(empty_columns, axis=1)
p2.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 40434 entries, 0 to 40433
Columns: 344 entries, rowid to releasedate
dtypes: float64(235), int64(23), object(86)
memory usage: 106.4+ MB

threshold = 20000

# Identify columns to drop
columns_to_drop = [col for col in p2.columns if p2[col].isna().sum() >threshold]

# Drop the columns from the DataFrame
p3 = p2.drop(columns=columns_to_drop)

print(columns_to_drop)
print("Updated DataFrame shape:", p3.shape)

# Optionally save the cleaned dataset

['hd_name', 'hip_name', 'pl_orbper_reflink', 'pl_orbsmaxerr1', 'pl_orbsmaxerr2', 'pl_orbsmax_reflink', 'pl_rade_reflink', 'pl_radj', 'pl_radjerr1', 'pl_radjerr2', 'pl_radjlim', 'pl_radj_reflink', 'pl_bmasse', 'pl_bmasseerr1', 'pl_bmasseerr2', 'pl_bmasselim', 'pl_bmasse_reflink', 'pl_bmassj', 'pl_bmassjerr1', 'pl_bmassjerr2', 'pl_bmassjlim', 'pl_bmassj_reflink', 'pl_bmassprov', 'pl_dens', 'pl_denserr1', 'pl_denserr2', 'pl_denslim', 'pl_dens_reflink', 'pl_orbeccenerr1', 'pl_orbeccenerr2', 'pl_orbeccen_reflink', 'pl_insol', 'pl_insolerr1', 'pl_insolerr2', 'pl_insol_reflink', 'pl_eqt', 'pl_eqterr1', 'pl_eqterr2', 'pl_eqt_reflink', 'pl_orbincl', 'pl_orbinclerr1', 'pl_orbinclerr2', 'pl_orbincl_reflink', 'pl_tranmid_systemref', 'pl_tranmid_reflink', 'pl_imppar', 'pl_impparerr1', 'pl_impparerr2', 'pl_imppar_reflink', 'pl_trandeperr1', 'pl_trandeperr2', 'pl_trandep_reflink', 'pl_trandur_reflink', 'pl_ratdor', 'pl_ratdorerr1', 'pl_ratdorerr2', 'pl_ratdor_reflink', 'pl_ratror_reflink', 'pl_occdep', 'pl_occdeperr1', 'pl_occdeperr2', 'pl_occdeplim', 'pl_occdep_reflink', 'pl_orbtper', 'pl_orbtpererr1', 'pl_orbtpererr2', 'pl_orbtperlim', 'pl_orbtper_systemref', 'pl_orbtper_reflink', 'pl_orblper', 'pl_orblpererr1', 'pl_orblpererr2', 'pl_orblperlim', 'pl_orblper_reflink', 'pl_rvamp', 'pl_rvamperr1', 'pl_rvamperr2', 'pl_rvamplim', 'pl_rvamp_reflink', 'pl_projobliq', 'pl_projobliqerr1', 'pl_projobliqerr2', 'pl_projobliqlim', 'pl_projobliq_reflink', 'pl_trueobliq', 'pl_trueobliqerr1', 'pl_trueobliqerr2', 'pl_trueobliqlim', 'pl_trueobliq_reflink', 'st_spectype', 'st_spectype_reflink', 'st_teff_reflink', 'st_rad_reflink', 'st_mass_reflink', 'st_met_reflink', 'st_lum', 'st_lumerr1', 'st_lumerr2', 'st_lumlim', 'st_lum_reflink', 'st_logg_reflink', 'st_age', 'st_ageerr1', 'st_ageerr2', 'st_age_reflink', 'st_denserr1', 'st_denserr2', 'st_dens_reflink', 'st_vsin', 'st_vsinerr1', 'st_vsinerr2', 'st_vsinlim', 'st_vsin_reflink', 'st_rotp', 'st_rotperr1', 'st_rotperr2', 'st_rotplim', 'st_rotp_reflink', 'st_radv', 'st_radverr1', 'st_radverr2', 'st_radvlim', 'st_radv_reflink', 'ra_reflink', 'sy_pm_reflink', 'sy_dist_reflink', 'sy_plx_reflink', 'sy_bmag_reflink', 'sy_vmag_reflink', 'sy_jmag_reflink', 'sy_hmag_reflink', 'sy_kmag_reflink', 'sy_umag', 'sy_umagerr1', 'sy_umagerr2', 'sy_umag_reflink', 'sy_gmag', 'sy_gmagerr1', 'sy_gmagerr2', 'sy_gmag_reflink', 'sy_rmag', 'sy_rmagerr1', 'sy_rmagerr2', 'sy_rmag_reflink', 'sy_imag', 'sy_imagerr1', 'sy_imagerr2', 'sy_imag_reflink', 'sy_zmag', 'sy_zmagerr1', 'sy_zmagerr2', 'sy_zmag_reflink', 'sy_w1mag_reflink', 'sy_w2mag_reflink', 'sy_w3mag_reflink', 'sy_w4magerr1', 'sy_w4magerr2', 'sy_w4mag_reflink', 'sy_gaiamag_reflink', 'sy_icmag', 'sy_icmagerr1', 'sy_icmagerr2', 'sy_icmag_reflink', 'sy_tmag_reflink', 'sy_kepmag_reflink', 'pl_masse', 'pl_masseerr1', 'pl_masseerr2', 'pl_masselim', 'pl_massj', 'pl_massjerr1', 'pl_massjerr2', 'pl_massjlim', 'pl_msinie', 'pl_msinieerr1', 'pl_msinieerr2', 'pl_msinielim', 'pl_msinij', 'pl_msinijerr1', 'pl_msinijerr2', 'pl_msinijlim', 'pl_cmasse', 'pl_cmasseerr1', 'pl_cmasseerr2', 'pl_cmasselim', 'pl_cmassj', 'pl_cmassjerr1', 'pl_cmassjerr2', 'pl_cmassjlim']
Updated DataFrame shape: (40434, 155)

p3.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 40434 entries, 0 to 40433
Columns: 155 entries, rowid to releasedate
dtypes: float64(109), int64(23), object(23)
memory usage: 48.1+ MB

#Visualization-1
p3['sy_pnum'].isnull().sum()

0

p3['st_logg'].isnull().sum()

6529

VISUALIZATION -1

Gravitational Pull of Host Star Vs No.of Planets

#dropping all the rows which are having null values for this'st_logg' column
# VISUALIZATION-1
column_to_clean = 'st_logg' # Replace with the column name
p_clean = p3.dropna(subset=[column_to_clean])

plt.figure(figsize=(10, 6))
sns.lineplot(x=p_clean['st_logg'], y=p_clean['sy_pnum'], alpha=0.6, color='green')
plt.title('Gravitational Pull vs Number of Planets', fontsize=14)
plt.xlabel('Gravitational Pull ', fontsize=12)
plt.ylabel('Number of Planets', fontsize=12)
plt.grid(True)
plt.show()

VISUALIZATION-2

Discovery Methods vs Luminosity

# VISUALIZATION-2

p2['st_lum'].isnull().sum()

26431

#dropping all the rows which are having null values for this'st_lum' column
column_to_clean = 'st_lum' # Replace with the column name
p_clean = p2.dropna(subset=[column_to_clean])

#For Microlensing only 4 records found, Astrometry 2 records found,Disk Kinematics 2 records found.. 
#We are removing them in order to get Appropriate Visualizations...

values_to_remove = ["Microlensing","Astrometry","Disk Kinematics"]
p_clean['discoverymethod'] = p_clean['discoverymethod'].replace(values_to_remove, np.nan)

# Drop rows with NaN in the 'discoverymethod' column
p1_clean = p_clean.dropna(subset=['discoverymethod'])

<ipython-input-17-9ec2c2650897>:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  p_clean['discoverymethod'] = p_clean['discoverymethod'].replace(values_to_remove, np.nan)

plt.figure(figsize=(20, 10))
sns.boxplot(x=p1_clean['discoverymethod'], y=p1_clean['st_lum'], color='green')
plt.title('Discovery Methods vs Luminosity', fontsize=14)
plt.xlabel('Discovery Methods', fontsize=12)
plt.ylabel('Luminosity', fontsize=12)
plt.grid(True)
plt.show()

import plotly.express as px

VISUALIZATION -3

Discovery Method Vs Planet Types

# VISUALIZATION -3

if 'discoverymethod' in p2.columns and 'pl_letter' in p2.columns:
    # Count the occurrences of each planet type by discovery method
    planet_type_by_method = p2.groupby(['discoverymethod', 'pl_letter']).size().reset_index(name='count')

    # Create a bar chart to visualize the count of planet types by discovery method
    fig = px.bar(planet_type_by_method, x='discoverymethod', y='count', color='pl_letter', 
                 title='Discovery Methods vs Planet Types',
                 labels={'discoverymethod': 'Discovery Method', 'pl_letter': 'Planet Type', 'count': 'Count of Planets'},
                 barmode='stack')
    fig.update_layout(
        yaxis=dict(tickmode='linear', tick0=0, dtick=1000),# dtick sets the difference between ticks
        width=800,  # Set width of the plot
        height=600
    )

    fig.show()

   
else:
    print("Columns 'discoverymethod' or 'pl_type' not found in the dataset.")

VISUALIZATION-4

Stellar Luminosity vs Metallicity

p2["st_lum"].isnull().sum()

26431

p2["st_met"].isnull().sum()

13385

filtered_data = p2[['st_lum', 'st_met']].dropna() # removing all the rows with the null values of the mentioned columns

# Bin the metallicity values into discrete categories for the box plot
filtered_data['st_met_bins'] = pd.cut(filtered_data['st_met'], bins=5)

# Create the box plot
plt.figure(figsize=(12, 6))
sns.boxplot(data=filtered_data, x='st_met_bins', y='st_lum')
plt.title('Box Plot of Stellar Luminosity vs Metallicity Bins', fontsize=14)
plt.xlabel('Stellar Metallicity Bins [st_met]', fontsize=12)
plt.ylabel('Stellar Luminosity [st_lum]', fontsize=12)
plt.xticks(rotation=45)
plt.grid(alpha=0.3)
plt.show()

Visualization-5

Planet Discovery Locations in Sky

fig = px.scatter_geo(
    p3, 
    lat="glat", 
    lon="glon", 
    color="pl_letter",  # Color by planet type (e.g., 'pl_letter' or other suitable column)
    #size="pl_rade",  # Optional: size of the points according to planet radius
    hover_name="pl_name", 
    title="Planet Discovery Locations in the Sky",
    opacity=0.2  # Set opacity of the dots to 50%
)

# Increase the size of the plot
fig.update_layout(
    width=800,   # Set the width of the plot (pixels)
    height=800,   # Set the height of the plot (pixels)
)
# Show the plot
fig.show()

Visualization-6

Distribution of Planet Types

fig = px.pie(p3, names="pl_letter", title="Distribution of Planet Types")
fig.show()

Visualizaton -7

No.of Exo Planets Discovered Over Time

discovery_trend = df1.groupby('disc_year').size()

# Plotting
plt.figure(figsize=(10, 4))
sns.barplot(x=discovery_trend.index, y=discovery_trend.values, palette='viridis')
plt.title('Number of Exoplanets Discovered Over Time', fontsize=16)
plt.xlabel('Discovery Year', fontsize=12)
plt.ylabel('Number of Exoplanets', fontsize=12)
plt.xticks(rotation=45)
plt.show()